What Is NumPy? | Python NumPy Explained | #Shorts | #Simplilearn

Simplilearn · Beginner ·📊 Data Analytics & Business Intelligence ·1mo ago

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Introduces NumPy for Python, explaining its basics and importance in data analysis

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Imagine doing 1,000 sums on a basic calculator one by one. That's regular Python. Now imagine doing all 1,000 in 1 second on computer. Now that's NumPy. So now you know Python, right? You've done the loops, the functions, all of that. But then someone says, "Use NumPy." And you're like, "What is even [music] that?" So NumPy is basically a Python library, but not like a random one. It's the [music] one that makes Python actually handle numbers properly, like at scale. The thing is Python by itself is slow with numbers. Like if you have a million values and you're looping through them one by one, [music] your code is going to take forever. And that's not just practical when you're working with real data. >> [music] >> And the way it does that is through arrays. Instead of a regular Python list, NumPy stores all your numbers together in memory, same type, one clean block, and your computer just loves that. It can process it so much faster. Up to 1,000 times faster than regular Python. Now that sounds like a lot, but yes, that's exactly what it is. And with that speed, you can do matrix math, linear algebra, statistical operations, reshape your data, stuff that would take you like 30 lines [music] of Python, NumPy handles in literally one line. And that's why every single major library in data and AI, Pandas, TensorFlow, scikit-learn, they're all built on top of NumPy. Like under the hood, NumPy is always there. You're probably [music] already using it without even realizing it. So if you're learning Python and you want to get into data science or machine learning or anything serious with numbers, NumPy is where you [music] start. It's not optional. That's just the foundation. Hope that's clear now. If you like this video, do subscribe, drop a like, and keep following because there's a lot more video coming next. See you in the next one.

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